Private vLLM inference for your product
Consulting to deploy private inference, connect the API to your product and organise capacity, access and operations.
The challenge
An API responding in an initial test does not prove it can handle your product's workload. Context length, concurrent requests and attention cache change memory use.
Approach
- Assess the model, available infrastructure and product requirements.
- Size context, concurrency and memory using a representative workload.
- Integrate the API with controlled access, identified versions and monitoring.
Consulting scope
The proposal can include deployment, integration, capacity criteria, updates and recovery, with documentation for the team.
Measurements cover queueing, time to first token and behaviour under concurrency. Hardware, models and traffic determine sizing.
Application in your company
Suitable for companies that need to operate models on owned or rented infrastructure, connect inference to a product and assign responsibility for access, updates, observability and recovery.
Consulting for your project
Infrastructure review, deployment and ongoing operations, with scope and pricing defined in the proposal.
Consulting
Infrastructure review, deployment and management for your company's AI systems.